Constructing High-Accuracy Letter-to-Phoneme Rules with Machine Learning

  • Ghulum Bakiri
  • Thomas G. Dietterich
Part of the Telecommunications Technology & Applications Series book series (TTAP)


This chapter describes a machine learning approach to the problem of letter-to-sound conversion that builds upon and extends the pioneering Nettalk work of Sejnowski and Rosenberg. Among the many extensions to the NETtalk system were the following: a different learning algorithm, a wider input window, errorcorrecting output coding, a right-to-left scan of the word to be pronounced (with the results of each decision influencing subsequent decisions), and the addition of several useful input features. These changes yielded a system that performs much better than the original Nettalk. After training on 19,002 words, the system achieves 93.7% correct pronunciation of individual phonemes and 64.8% correct pronunciation of whole words (where the pronunciation must exactly match the dictionary pronunciation to be correct) on an unseen 1000-word test set. Based on the judgements of three human listeners in a blind assessment study, our system was estimated to have a serious error rate of 16.7% (on whole words) compared to 26.1% for the DECtalk 3.0 rule base.


Decision Tree Binary Feature Output Representation Input Representation Human Judge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Ghulum Bakiri
    • 1
  • Thomas G. Dietterich
    • 2
  1. 1.Department of Computer ScienceUniversity of BahrainBahrain
  2. 2.Department of Computer ScienceOregon State UniversityUSA

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